AI-Powered Parking Spot Detection System for Real-Time Parking Management

Overview

Urban drivers can spend up to a third of their trip circling for a parking space, wasting fuel, time, and patience. The Smart Parking Spot Detection System is a computer-vision platform that transforms ordinary CCTV cameras into intelligent sensors, instantly revealing which bays are free or occupied. By streaming live availability to both lot operators and incoming drivers, the solution streamlines traffic flow in shopping malls, airports, hospitals, and corporate campuses without the expense of digging up asphalt for in-ground sensors.

Client Objective

Our client—a regional parking-management company that oversees several multi-storey garages—sought to eliminate congestion at peak hours, raise turnover on premium spaces, and capture reliable utilisation data for future capacity planning. They specifically wanted a solution that could be deployed during a single overnight maintenance window, leveraged their existing camera network, and required minimal day-to-day oversight by on-site staff.

Our Approach

We began by auditing each facility’s camera layout, lighting conditions, and network bandwidth to ensure video feeds could be processed with low latency. A dataset of fifteen thousand annotated frames was then curated, representing daytime, night-time, and wet-weather scenarios across four very different lots. Using this corpus, we fine-tuned a YOLOv8 object-detection model to classify every marked bay in less than eighty milliseconds per frame. To keep bandwidth costs down we adopted an edge-first architecture: a lightweight Python service runs on inexpensive Jetson Nano boxes mounted in the control room, performs real-time inference, and publishes JSON events to a cloud API. The cloud layer aggregates results, applies business rules (such as dynamic pricing triggers), and serves both a React dashboard and a REST endpoint that the client can embed in its mobile app.

Key Features

Unlike traditional sensor grids, our platform relies solely on existing IP cameras, allowing a car park of two thousand spaces to go live overnight. A single web dashboard provides colour-coded occupancy overlays, historical utilisation graphs, rule-based alerts, and multi-lot switching for regional supervisors. Because every detection event is published through a versioned REST API and an optional WebSocket stream, third-party developers can build consumer-facing apps or tie the data into city-wide smart-mobility platforms. The system is also designed for horizontal scaling: adding a new facility is as simple as registering its camera RTSP addresses, drawing polygons around each bay in the admin tool, and letting the model fine-tune itself on collected samples.

Impact

By the end of the first quarter the client reported a fifty-percent reduction in average search time, a twelve-percent uplift in revenue per bay during peak periods, and a seventeen-percent drop in idle-time carbon emissions thanks to fewer cars cruising the aisles.